Revert sparray adoption

Foggalong · Jul 24, 2024 · 2800068 · 2800068
1 parent fe08695
commit 2800068
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Showing 3 changed files with 20 additions and 20 deletions.
diff --git a/robustocs/loaders.py b/robustocs/loaders.py
@@ -79,7 +79,7 @@ def load_symmetric_matrix(filename: str, dimension: int
 
 
 def load_symmetric_matrix_coo(filename: str, dimension: int, nnz: int
-                              ) -> sparse.sparray:
+                              ) -> sparse.spmatrix:
     """
     Since neither NumPy or SciPy have a stock way to load symmetric matrices
     into sparse coordinate format, this adds one.
@@ -126,15 +126,15 @@ def load_symmetric_matrix_coo(filename: str, dimension: int, nnz: int
 
             index += 1
 
-    return sparse.coo_array(
+    return sparse.coo_matrix(
         (vals, (rows, cols)),
         shape=(dimension, dimension),
         dtype=np.floating
     )
 
 
 def load_symmetric_matrix_csr(filename: str, dimension: int, nnz: int
-                              ) -> sparse.sparray:
+                              ) -> sparse.spmatrix:
     """
     Loads a symmetric matrix into compressed sparse row format. It does this
     by first loading into sparse coordinate format and then converting with
@@ -157,7 +157,7 @@ def load_symmetric_matrix_csr(filename: str, dimension: int, nnz: int
     """
 
     matrix = load_symmetric_matrix_coo(filename, dimension, nnz)
-    return sparse.csr_array(matrix)
+    return sparse.csr_matrix(matrix)
 
 
 def load_ped(filename: str) -> dict[int, list[int]]:
@@ -238,9 +238,9 @@ def load_problem(A_filename: str, E_filename: str, S_filename: str,
                  nnzA: int | None = None, nnzS: int | None = None,
                  dimension: int | None = None, pedigree: bool = False,
                  issparse: bool = False
-                 ) -> tuple[npt.NDArray[np.floating] | sparse.sparray,
+                 ) -> tuple[npt.NDArray[np.floating] | sparse.spmatrix,
                             npt.NDArray[np.floating],
-                            npt.NDArray[np.floating] | sparse.sparray,
+                            npt.NDArray[np.floating] | sparse.spmatrix,
                             int]:
     """
     Load a robust genetic selection problem into Python.
@@ -274,19 +274,19 @@ def load_problem(A_filename: str, E_filename: str, S_filename: str,
 
     Returns
     -------
-    ndarray or sparray
+    ndarray or spmatrix
         Covariance matrix of candidates in the cohort.
     ndarray
         Vector of expected values of the expected breeding values of
         candidates in the cohort.
-    ndarray or sparray
+    ndarray or spmatrix
         Covariance matrix of expected breeding values of candidates in the
         cohort.
     int
         Dimension of the problem.
     """
 
-    E = np.loadtxt(E_filename, dtype=float)
+    E = np.loadtxt(E_filename, dtype=np.floating)
     # if dimension not specified, use `E` which doesn't need preallocation
     if not dimension:
         assert isinstance(E.size, int)  # catches E being empty
@@ -307,7 +307,7 @@ def load_problem(A_filename: str, E_filename: str, S_filename: str,
         A = makeA(load_ped(A_filename))
         # HACK this loads the full matrix, then converts it down to sparse
         if issparse:
-            A = sparse.coo_array(A)
+            A = sparse.coo_matrix(A)
     else:
         if issparse:
             if not nnzA:

diff --git a/robustocs/solvers.py b/robustocs/solvers.py
@@ -23,7 +23,7 @@
 
 
 def gurobi_standard_genetics(
-    sigma: npt.NDArray[np.floating] | sparse.sparray,
+    sigma: npt.NDArray[np.floating] | sparse.spmatrix,
     mu: npt.NDArray[np.floating],
     sires,  # type could be np.ndarray, sets[ints], lists[int], range, etc
     dams,   # type could be np.ndarray, sets[ints], lists[int], range, etc
@@ -141,9 +141,9 @@ def gurobi_standard_genetics(
 
 
 def gurobi_robust_genetics(
-    sigma: npt.NDArray[np.floating] | sparse.sparray,
+    sigma: npt.NDArray[np.floating] | sparse.spmatrix,
     mubar: npt.NDArray[np.floating],
-    omega: npt.NDArray[np.floating] | sparse.sparray,
+    omega: npt.NDArray[np.floating] | sparse.spmatrix,
     sires,  # type could be np.ndarray, sets[ints], lists[int], range, etc
     dams,   # type could be np.ndarray, sets[ints], lists[int], range, etc
     lam: float,  # cannot be called `lambda`, that's reserved in Python
@@ -259,7 +259,7 @@ def gurobi_robust_genetics(
     M[0, sires] = 1
     M[1, dams] = 1
     # define the right hand side of the constraint Mx = m
-    m = np.full(2, 0.5, dtype=float)
+    m = np.full(2, 0.5, dtype=np.floating)
     model.addConstr(M@w == m, name="sum-to-half")
 
     # conic constraint which comes from robust optimization
@@ -280,9 +280,9 @@ def gurobi_robust_genetics(
 
 
 def gurobi_robust_genetics_sqp(
-    sigma: npt.NDArray[np.floating] | sparse.sparray,
+    sigma: npt.NDArray[np.floating] | sparse.spmatrix,
     mubar: npt.NDArray[np.floating],
-    omega: npt.NDArray[np.floating] | sparse.sparray,
+    omega: npt.NDArray[np.floating] | sparse.spmatrix,
     sires,  # type could be np.ndarray, sets[ints], lists[int], range, etc
     dams,   # type could be np.ndarray, sets[ints], lists[int], range, etc
     lam: float,  # cannot be called `lambda`, that's reserved in Python
@@ -376,7 +376,7 @@ def gurobi_robust_genetics_sqp(
     ndarray
         Portfolio vector which Gurobi has determined is a solution.
     float
-        Auxiliary variable corresponding to uncertainty associated with the
+        Auxillary variable corresponding to uncertainty associated with the
         portfolio vector which Gurobi has determined is a solution.
     float
         Value of the objective function for returned solution vector.
@@ -453,7 +453,7 @@ def gurobi_robust_genetics_sqp(
 
 def highs_bound_like(dimension: int,
                      value: float | list[float] | npt.NDArray[np.floating]
-                     ):  # BUG -> npt.NDArray[np.floating] | list[float]
+                     ):  # -> npt.NDArray[np.floating] | list[float] # BUG broke
     """
     Helper function which allows HiGHS to interpret variable bounds specified
     either as a vector or a single floating point value. If `value` is an array

diff --git a/robustocs/utils.py b/robustocs/utils.py
@@ -107,7 +107,7 @@ def obj_string(name: str, value: float, precision: int,
 def check_uncertainty_constraint(
     z: float,
     w: npt.NDArray[np.floating],
-    omega: npt.NDArray[np.floating] | sparse.sparray,
+    omega: npt.NDArray[np.floating] | sparse.spmatrix,
     tol: float = 1e-7,
     debug: bool = False
 ) -> bool:
@@ -123,7 +123,7 @@ def check_uncertainty_constraint(
     Parameters
     ----------
     z : float
-        Auxiliary variable from a solution to the robust selection problem.
+        Auxillary variable from a solution to the robust selection problem.
     w : ndarray
         Portfolio vector from a solution to the robust selection problem.
     omega : ndarray